RESERVOIR MANAGEMENT & IMPROVED RECOVERY

ABSTRACT
Summary
Intelligent completions are focused on the delivery and management of increased
production flexibility. Intelligent Well systems Technology (IWsT) delivers the
ability to install, operate, monitor and control completions without the need for
conventional interventions. Multi-zone intelligent-well completions contain
appropriate monitoring devices located between zonal isolation packers. They
control the flow into or out of each zone with Interval Control Valves (ICV5).
Installation of intelligence at the well level is, unfortunately, not always a
guarantee for success - it must add value to gain acceptance. This paper starts
from the premise that some reservoir types are inherently more suited to IWsT
application than others.
A reservoir model should predict the hydrocarbon distribution and the reservoir
flow properties. The internal-makeup of a reservoir provides a framework of
connectivity and fluid flow pathways through the reservoir. The spatial distribution
of flow units and barriers, such as faults, can be represented within a reservoir
model. A probabilistic reservoir model tries to capture reality by logical relations;
but is limited by our perception, knowledge and understanding of reality. It is,
therefore, the “best-guess”.
A series of generic reservoir types based on property distributions derived from
field data, have been built. They were tested to determine the added value from
IWsT compared to standard well completions. Situations in which IWsT proved
particularly successful have been identified. Results show that IWsT can control
uneven, invading fluid-fronts that develop along the length of the wellbore due to
permeability differences, reservoir compartmentalisation or different strength
aquifer/gas cap support. Recovery improves and water production reduces with
the correct choice of ICVs (Downhole Interval Control Valves) number, position
and length of zone being controlled. However, the degree of improvement is
dependent on reservoir type and differs from one reservoir model to another.
Results from this study have helped to develop a selection criterion for improved
implementation of IWsT.
Introduction
An intelligent well is a well with the ability to control the production flow by a
down-hole choke. This is managed through real time monitoring and control of
the producing zones using installed Inflow Control Valves (ICV) and an optimised
sensor distribution for data acquisition and down-hole fluid production
measurement. It also has the ability to shut off water/gas producing zone at the
wellbore. It produces single or multiple zones into one wellbore, leading to
commingled production from different zones and lateral bores.
Intelligent Completions, by commingling stacked pay, manages the (possibly
tilted) oil rims with different thickness in multiple fault blocks from a single well

It can produce a scale of multiple realisations that all fit the basic data
and field information. These include:
1. minimising capital and operating costs while reducing risk. IWsT can decrease the risk and
uncertainty associated with the production of complicated reservoirs. It is based on upscaled
well log data Stochastic Petrophysical Modeling generates multiple equi-probable
realizations of the reservoir. reservoir performance. Identifying the type of reservoir where the
IWsT can be applied is an important step in evaluating its application. This requires skills in reservoir
characterisation.bore. Deterministic Petrophysical Modeling uses interpolation to assign
a property value to cells that are not penetrated by wells. Weber and Geuns
proposed that reservoirs could be grouped into three basic reservoir types’:
Layer Cake Reservoir type. Jigsaw Puzzle Reservoir type and Labyrinth
Reservoir type.
A key issue in the reservoir management process is geological uncertainty.
A useful tool for this stage is the classification of reservoirs.
IWsT. operating at or near real-time. A faulted Reservoir with transmissive and sealing faults and a range of OilWater contacts and Reservoir Pressures
. stochastic realisations of a heterogeneous. well performance and field development. Figure 1 illustrates the commingled production from separate sands using
an intelligent well. single reservoir with no
faulting or reservoir dip
3. where there is heterogeneity below the facies scale.
Methodology
A range of reservoir models have been studied. single reservoir
2.
Intelligent completion techniques are gaining popularity because of their reservoir
monitoring and well / field performance management capabilities while
minimizing the well intervention requirements.a crucial
stage in reservoir management. It involves
taking decisions and predicting their future consequences — hence the need to
model the reservoir’s future behaviour. The ultimate goal for
this continuous monitoring of the reservoir is to implement a more proactive style
of reservoir management technique. Multiple.
Stochastic Modeling is a helpful tool for modeling the uncertain aspects of a
reservoir. maximising
recovery. Knowing
where to apply this technology begins with reservoir characterization. Reservoir
management objectives include increasing production and reserves. enables high quality.
interpretation and reaction in a continuous feedback loop. well surveillance. A constant permeability.

The optimization objective in different scenarios was to maximize oil recovery
while minimizing water production by managing the ICy setting. Figure 3a shows a simple flat model with inclined
layers. increasing
levels of cross-flow & communication have been achieved between the layers in
Figure 3b by progressively removing the shale barriers.
Figure 5 shows extra recovery with IWsT from the high and permeability layers compared
to the conventional completion shown 4. The % recovery — and the scale of the increase — vary from a
reservoir model to another and is a function of the distribution of porosity and
permeability in the models.4. Figure 2
illustrates the well configuration modeled in Eclipse. A channel sand model
The production from long horizontal well(s) modeled in the above reservoir types
was simulated using the multi-segment option in Eclipse Simulator.
. Figure 6 illustrates the reduction in water
production using intelligent The full results will be discussed in the paper and
presentation.g.
Results
Results from this study showed that I-Well gives significantly better recovery in
most cases. Multiple deterministic and stochastic realisations of heterogeneous inclined
and layered reservoirs with a range of dip angles
5. A variety of model
configurations have been used to illustrate the versatility of IWsT e. A horizontal well is placed near the top of the model. The optimization
policy was kept simple so that it was possible to compare the different scenarios.

IWsT can control
uneven. the applicability of Intelligent Wells has been classified and
discussed as a function of the reservoir type.Conclusions
IWsT has been shown to be capable of managing geological variability and thus
coping with geological uncertainty in a wide range of reservoirs. 1248-1253. “Framework for Constructing Clastic
Reservoir Simulation Models”. JPT.C. invading fluid-fronts that develop along the length of the wellbore due to
permeability differences. reservoir compartmentalisation or different strength
aquifer/gas cap support.
In the full paper. Weber K. 42(10). and Van Geuns L. 1296-1297
.J.
References
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